In view of the differences in appearance and the complex backgrounds of crop diseases, automatic identification of field diseases is an extremely challenging topic in smart agriculture. To address this challenge, a popular approach is to design a Deep Convolutional Neural Network (DCNN) model that extracts visual disease features in the images and then identifies the diseases based on the extracted features. This approach performs well under simple background conditions, but has low accuracy and poor robustness under complex backgrounds. In this paper, an end-to-end disease identification model composed of a disease-spot region detector and a disease classifier (YOLOv5s + BiCMT) was proposed. Specifically, the YOLOv5s network was used to detect the disease-spot regions so as to provide a regional attention mechanism to facilitate the disease identification task of the classifier. For the classifier, a Bidirectional Cross-Modal Transformer (BiCMT) model combining the image and text modal information was constructed, which utilizes the correlation and complementarity between the features of the two modalities to achieve the fusion and recognition of disease features. Meanwhile, the problem of inconsistent lengths among different modal data sequences was solved. Eventually, the YOLOv5s + BiCMT model achieved the optimal results on a small dataset. Its Accuracy, Precision, Sensitivity, and Specificity reached 99.23, 97.37, 97.54, and 99.54%, respectively. This paper proves that the bidirectional cross-modal feature fusion by combining disease images and texts is an effective method to identify vegetable diseases in field environments.

The development of resistant cucumber varieties is of a great importance for reducing the production loss caused by root-knot nematodes. After cucumber plants are infected with root-knot nematodes, their roots will swell into spherical bumps. Rapid and accurate detection of the infected sites and assessment of the disease severity play a key role in selecting resistant cucumber varieties. Because the locations and sizes of the spherical bumps formed after different degrees of infection are random, the currently available detection and counting methods based on manual operation are extremely time-consuming and labor-intensive, and are prone to human error. In response to these problems, this paper proposes a cucumber root-knot nematode detection model based on the modified YOLOv5s model (i.e., YOLOv5-CMS) in order to support the breeding of resistant cucumber varieties. In the proposed model, the dual attention module (CBAM-CA) was adopted to enhance the model’s ability of extracting key features, the K-means++ clustering algorithm was applied to optimize the selection of the initial cluster center, which effectively improved the model’s performance, and a novel bounding box regression loss function (SIoU) was used to fuse the direction information between the ground-truth box and the predicted box so as to improve the detection precision. The experiment results show that the recall (R) and mAP of the YOLOv5s-CMS model were improved by 3% and 3.1%, respectively, compared to the original YOLOv5s model, which means it can achieve a better performance in cucumber root-knot nematode detection. This study provides an effective method for obtaining more intuitive and accurate data sources during the breeding of cucumber varieties resistant to root-knot nematode.

In order to integrate fragmented text data of crop disease knowledge to solve the current problems of disordered knowledge management, weak correlation and difficulty in knowledge sharing, a Chinese named-entity–relation-extraction model for crop diseases (BBCPF) was proposed in this paper by utilizing the advantage of knowledge graph in describing complex relations between disease entities in a structured form. This model was composed of two parts, i.e., named-entity recognition and relation extraction, in the form of an assembly line. To deal with the different meanings of Chinese crop disease terms in different contexts and to better obtain the contextual information, the BERT model was introduced for dynamic vector representations. Then, the BiLSTM layer was used to learn long-distance text information, and the CRF was applied to obtain the globally optimal labeling sequence, so as to output the crop disease entities. According to the entity category, the entities were divided as subjects and objects, which were then input into the disordered language model PERT to extract the contextual features of the relation data. At last, the fully connected layer was used to decode the information and output the crop disease entity-relation triples. The experiment results show that, on the self-built disease corpus dataset, the Precision, Recall, and F1-Score values of the established model reached 85.63%, 79.46% and 82.43%, respectively, for entity extraction, and reached 97.96%, 98.43% and 98.16%, respectively, for relation extraction. This paper provides an effective method for information extraction in the construction of Chinese crop disease domain knowledge graphs.

Disease severity grading is the primary decision-making basis for the amount of pesticide usage in vegetable disease prevention and control. Based on deep learning, this paper proposed an integrated framework, which automatically segments the target leaf and disease spots in cucumber images using different semantic segmentation networks and then calculates the area of disease spots and the target leaf for disease severity grading. Two independent datasets of leaves and lesions were constructed, which served as the training set for the first-stage diseased leaf segmentation and the second-stage lesion segmentation models. The leaf dataset contains 1140 images, and the lesion data set contains 405 images. The proposed TRNet was composed of a convolutional network and a Transformer network and achieved an accuracy of 93.94% by fusing local features and global features for leaf segmentation. In the second stage, U-Net (Resnet50 as the feature network) was used for lesion segmentation, and a Dice coefficient of 68.14% was obtained. After integrating TRNet and U-Net, a Dice coefficient of 68.83% was obtained. Overall, the two-stage segmentation network achieved an average accuracy of 94.49% and 94.43% in the severity grading of cucumber downy mildew and cucumber anthracnose, respectively. Compared with DUNet and BLSNet, the average accuracy of TUNet in cucumber downy mildew and cucumber anthracnose severity classification increased by 4.71% and 8.08%, respectively. The proposed model showed a strong capability in segmenting cucumber leaves and disease spots at the pixel level, providing a feasible method for evaluating the severity of cucumber downy mildew and anthracnose.

As one of the major global food crops, the monitoring and management of the winter wheat planting area is of great significance for agricultural production and food security worldwide. Today, the development of high-resolution remote sensing imaging technology has provided rich sources of data for extracting the visual planting information of winter wheat. However, the existing research mostly focuses on extracting the planting plots that have a simple terrain structure. In the face of diverse terrain features combining mountainous areas, plains, and saline alkali land, as well as small-scale but complex planting structures, the extraction of planting plots through remote sensing imaging is subjected to great challenges in terms of recognition accuracy and model complexity. In this paper, we propose a modified Segformer model for extracting winter wheat planting plots with complex structures in rural areas based on the 0.8 m high-resolution multispectral data obtained from the Gaofen-2 satellite, which significantly improves the extraction accuracy and efficiency under complex conditions. In the encoder and decoder of this method, new modules were developed for the purpose of optimizing the feature extraction and fusion process. Specifically, the improvement measures of the proposed method include: (1) The MixFFN module in the original Segformer model is replaced with the Multi-Scale Feature Fusion Fully-connected Network (MSF-FFN) module, which enhances the model’s representation ability in handling complex terrain features through multi-scale feature extraction and position embedding convolution; furthermore, the DropPath mechanism is introduced to reduce the possibility of overfitting while improving the model’s generalization ability. (2) In the decoder part, after fusing features at four different scales, a CoordAttention module is added, which can precisely locate important regions with enhanced features in the images by utilizing the coordinate attention mechanism, therefore further improving the model’s extraction accuracy. (3) The model’s input data are strengthened by incorporating multispectral indices, which are also conducive to the improvement of the overall extraction accuracy. The experimental results show that the accuracy rate of the modified Segformer model in extracting winter wheat planting plots is significantly increased compared to traditional segmentation models, with the mean Intersection over Union (mIOU) and mean Pixel Accuracy (mPA) reaching 89.88% and 94.67%, respectively (an increase of 1.93 and 1.23 percentage points, respectively, compared to the baseline model). Meanwhile, the parameter count and computational complexity are significantly reduced compared to other similar models. Furthermore, when multispectral indices are input into the model, the mIOU and mPA reach 90.97% and 95.16%, respectively (an increase of 3.02 and 1.72 percentage points, respectively, compared to the baseline model).